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FromTheMoon [43]
3 years ago
10

Choose a problem that lends to an implementation that uses dynamic programming. Clearly state the problem and then provide high-

level pseudocode for the algorithm. Explain why this algorithm can benefit from dynamic programming. Try to choose an algorithm different from any already posted by one of your classmates.
Computers and Technology
1 answer:
Svetradugi [14.3K]3 years ago
8 0

Answer:

Explanation:

The maximum weighted independent collection of vertices in a linear chain graph is a straightforward algorithm whereby dynamic programming comes in handy.

Provided a linear chain graph G = (V, E, W), where V is a collection of vertices, E is a set of edges margins, and W is a weight feature function applied to each verex.  Our goal is to find an independent collection of vertices in a linear chain graph with the highest total weight of vertices in that set.

We'll use dynamic programming to do this, with L[k] being the full weighted independent collection of vertices spanning from vertex 1 \to vertex k.

If we add vertex k+1 at vertex k+1, we cannot include vertex k, and thus L[k+1] would either be equivalent to L[k] when vertex k+1 is not being used, or L[k+1] = L[k-1] + W[k+1] when vertex k+1 is included.

Thus, L[k+1] = max \{ L[k], \ L[k-1] + W[k+1] \}

As a result, the dynamic programming algorithm technique can be applied in the following way.

ALGO(V, W, n) // V is a linearly ordered series of n vertices with such a weight feature W

\text{1. L[0] = 0, L[1] = W[1], L[2] = max{W[1], W[2]} //Base cases} \\ \\ \text{2. For i = 3 to n:- \\} \\ \\\text{3........ if ( L[i-1] > L[i-2] + W[ i ] )} \\ \\ \text{4............Then L[ i ] = L[i-1]} \\ \\ \text{5.........else} \\ \\ \text{6................L[i] = L[i-2] + W[i] }\\ \\ \text{7. Return L[n] //our answer.}

As a result, using dynamic programming, we can resolve the problem in O(n) only.

This is an example of a time-saving dynamic programming application.

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